F
Francisco F. Rivera
Researcher at University of Santiago de Compostela
Publications - 120
Citations - 948
Francisco F. Rivera is an academic researcher from University of Santiago de Compostela. The author has contributed to research in topics: Parallel algorithm & Hypercube. The author has an hindex of 15, co-authored 119 publications receiving 896 citations. Previous affiliations of Francisco F. Rivera include New York State Department of Health & University of Santiago, Chile.
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High performance genetic algorithm for land use planning
TL;DR: This work focuses on implementing and analyzing different parallel paradigms: multi-core parallelism, cluster parallelism and the combination of both, and some tests were performed that show the suitability of genetic algorithms to land use planning problems.
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Research Article: A GIS-embedded system to support land consolidation plans in Galicia
Juan Touriño,Jorge Parapar,Ramón Doallo,M. Boullón,Francisco F. Rivera,Javier D. Bruguera,Xesús P. González,Rafael Crecente,C. J. Álvarez +8 more
TL;DR: The system provides an integrated framework for the management of spatial and administrative consolidation information and includes optimization-based algorithms for the automated generation of multiple alternative parcel reallocations, as well as an environment to refine and objectively evaluate the proposed solutions.
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Image segmentation based on merging of sub-optimal segmentations
TL;DR: A heuristic segmentation algorithm is presented based on the oversegmentation of an image that produces high quality global segmentations from a set of low quality segmentations with reduced execution times.
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Optimization of sparse matrix-vector multiplication using reordering techniques on GPUs
TL;DR: It is found that SpMV is very sensitive to the application of reordering techniques on GPUs, and in most of the cases, reordered matrices outperform the original ones, showing noticeable speedups up to 2.6x.
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Fuzzy sets-based classification of electron microscopy images of biological macromolecules with an application to ribosomal particles.
José María Carazo,Francisco F. Rivera,Francisco F. Rivera,Emilio L. Zapata,Emilio L. Zapata,Michael Radermacher,Joachim Frank +6 more
TL;DR: Pattern recognition methods based on the theory of fuzzy sets are tested for their ability to classify electron microscopy images of biological specimens and some conclusions about the consistency of these classifications will be drawn from this comparison.